Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction

Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction

Show other versions (1)

This study utilized a range of machine learning algorithms to predict the hourly streamflow in the Ikpoba River. Data gathering relied on a Hydromet System installed along the river, collecting hourly measurements of gage height, ambient temperature, and atmospheric pressure. To convert the gage he...

Full description

Saved in:
Bibliographic Details
Main Authors: Osahon Idemudia, Jacob Odeh Ehiorobo, Christopher Osadolor Izinyon, Idowu Ilaboya
Format: Article
Language:English
Published: Can Tho University Publisher 2024-07-01
Series:CTU Journal of Innovation and Sustainable Development
Subjects:
Machine Learning, Random Forest (RF), Decision Tress (DT), Support Vector Regression (SVR), and Gradient Boosting (GB)
Online Access:https://ctujs.ctu.edu.vn/index.php/ctujs/article/view/827
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items